CN101611582A - Select the method and the Data Transmission Control Unit of transmission parameter for transfer of data - Google Patents
Select the method and the Data Transmission Control Unit of transmission parameter for transfer of data Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L1/00—Arrangements for detecting or preventing errors in the information received
- H04L1/0001—Systems modifying transmission characteristics according to link quality, e.g. power backoff
- H04L1/0009—Systems modifying transmission characteristics according to link quality, e.g. power backoff by adapting the channel coding
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- H—ELECTRICITY
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- H04B—TRANSMISSION
- H04B17/00—Monitoring; Testing
- H04B17/30—Monitoring; Testing of propagation channels
- H04B17/309—Measuring or estimating channel quality parameters
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- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L1/00—Arrangements for detecting or preventing errors in the information received
- H04L1/0001—Systems modifying transmission characteristics according to link quality, e.g. power backoff
- H04L1/0002—Systems modifying transmission characteristics according to link quality, e.g. power backoff by adapting the transmission rate
- H04L1/0003—Systems modifying transmission characteristics according to link quality, e.g. power backoff by adapting the transmission rate by switching between different modulation schemes
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- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L1/00—Arrangements for detecting or preventing errors in the information received
- H04L1/0001—Systems modifying transmission characteristics according to link quality, e.g. power backoff
- H04L1/0015—Systems modifying transmission characteristics according to link quality, e.g. power backoff characterised by the adaptation strategy
- H04L1/0016—Systems modifying transmission characteristics according to link quality, e.g. power backoff characterised by the adaptation strategy involving special memory structures, e.g. look-up tables
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- H04L1/00—Arrangements for detecting or preventing errors in the information received
- H04L1/004—Arrangements for detecting or preventing errors in the information received by using forward error control
- H04L1/0045—Arrangements at the receiver end
- H04L1/0047—Decoding adapted to other signal detection operation
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- H04L1/00—Arrangements for detecting or preventing errors in the information received
- H04L1/02—Arrangements for detecting or preventing errors in the information received by diversity reception
- H04L1/06—Arrangements for detecting or preventing errors in the information received by diversity reception using space diversity
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- H—ELECTRICITY
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- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L1/00—Arrangements for detecting or preventing errors in the information received
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Abstract
A kind of method of selecting transmission parameter for transfer of data is provided, be included as each the specified data throughput in a plurality of transmission parameter settings, described data throughout is the data throughout of expecting when described transmission parameter settings is used for transfer of data, wherein determines described data throughout by pregenerated, transmission parameter settings to the mapping of data throughout; And based on the desired data throughput selection transmission parameter settings of determining.
Description
Technical Field
The present invention generally relates to a method of selecting transmission parameters for data transmission and a data transmission controller.
Background
Adaptive Modulation and Coding (AMC) is an effective tool to combat fading and enhance the performance of wireless communication systems. The task of AMC is to select the best Modulation and Coding Scheme (MCS) based on Channel State Information (CSI) associated with the communication channel between a transmitter and a receiver, thereby achieving a higher communication system data throughput. Each MCS is associated with a coding rate and a constellation size, having a given bit rate. The Packet Error Rate (PER) of a data transmission between a transmitter and a receiver can be used as a basis for quality of service (QoS) constraints to select the best MCS for the current conditions of the communication channel. The PER determines the number of retransmissions needed, which affects throughput and transmission delay.
Disclosure of Invention
There is provided a method of selecting transmission parameters for data transmission, comprising determining a data throughput for a plurality of transmission parameter settings, the data throughput being a desired data throughput when the transmission parameters are used for data transmission, wherein the data throughput is determined by a pre-generated mapping of transmission parameter setting value data throughputs; and selecting a transmission parameter setting based on the determined desired data throughput.
Drawings
Exemplary embodiments of the present invention are explained below with reference to the drawings.
Fig. 1 shows a communication system according to an embodiment of the invention;
FIG. 2 shows a flow diagram according to an embodiment of the invention;
FIG. 3 illustrates a data transmission controller according to an embodiment of the present invention;
FIG. 4 illustrates a demodulation/decoding system according to an embodiment of the present invention;
FIG. 5 shows a graph of functions;
FIG. 6 shows a flow diagram according to an embodiment of the invention;
FIG. 7 shows a flow diagram according to an embodiment of the invention;
FIG. 8 shows a flow diagram according to an embodiment of the invention;
FIG. 9 shows a flow diagram according to an embodiment of the invention;
fig. 10 shows a data flow diagram of MCS selection according to an embodiment of the present invention.
Detailed Description
Fig. 1 shows a communication system 100 according to an embodiment of the invention.
The communication system 100 comprises a transmitter 101 and a receiver 102. Transmitter 101 includes a plurality of transmit antennas 103, each transmit antenna 103 coupled to a respective transmit circuit 104. Each transmission circuit 104 is provided with NtX1 signal vector
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A component of (1), wherein NtIs the number of transmit antennas 103. Each transmit circuit 104 transmits a signal vector using a respective antenna 103sSuch that the signal vectorsAll are sent. Transmitted signal vector by received NrX1 signal vector
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Is received by the transmitter 102 via a communication channel 108 through a plurality of receive antennas 105, each receive antenna 105 being coupled to a respective receive circuit 106. N is a radical ofrIndicating the number of receive antennas 105. Since suppose NrAnd NtGreater than 1, and thus communication system 100 is a MIMO (multiple input multiple output) system, e.g., Nt＝Nr4 or 8.
Each receiving antenna 105 receives a signal vectorrEach component is output by the receiving circuit 106.
For example, the communication system 100 is a communication system according to Wifi IEEE 802.11n, WiMax IEEE 802.16, or 3GPP LTE (third generation partnership project long term evolution).
In one embodiment, communication system 100 is a MIMO system associated with Orthogonal Frequency Division Multiplexing (OFDM). Various transmission modes may be considered for a given antenna setup. These transmission modes include space-time coding (STC), Space Division Multiplexing (SDM), and mixed SDM-STD modes.
The various transmission modes can be classified as open loop or closed loop. When only the receiver 102 knows the channel state information related to the communication channel 108, the transmission mode is considered an open loop transmission mode. When certain channel knowledge is known at the transmitter 101, the transmission mode is a closed loop transmission mode. When the transmitter 101 operates in the closed-loop mode, a Singular Value Decomposition (SVD) of the channel matrix may be performed to diagonalize the channel for each OFDM subcarrier, as will be explained in more detail below.
In the embodiments described below, it is assumed that communication system 100 uses SDM transmission modes designed for both open loop and closed loop operation. However, in other embodiments, other modes (STC and mixed SDM-STC) may be used. Non-uniform MCS (i.e., different modulation schemes for different spatial streams) and other error correction coding (e.g., Low Density Parity Check (LDPC) codes, Turbo codes, etc.) can also be included in the system model.
The settings for MCS (modulation and coding scheme) are, for example, settings for constellation, coding rate, and transmission mode.
In one embodiment, the receiver 102 runs an adaptive modulation and coding algorithm that determines the best MCS available for multiple antenna settings. Adaptive modulation and coding algorithms, for example, attempt to maximize the bit rate of a data transmission between transmitter 101 and receiver 102 under PER constraints (e.g., maximum allowed PER).
The PER is a function of the MCS, CSI, and packet length (i.e., the number of bits contained within a data packet). In this function, the number of parameters is usually very large. To reduce the complexity of PER prediction, these parameters may be mapped onto a Link Quality Metric (LQM) and then directly mapped to a PER through a lookup table. For each MCS, a range of LQM values can be defined that maximizes the data throughput of communication system 100.
The most common LQM is the instantaneous signal-to-noise ratio (SNR). The instantaneous signal-to-noise ratio is defined as the ratio of the average of the square modes of the received signal to the noise power. However, the PER for a given MCS may vary significantly for different channel realizations with the same instantaneous SNR. There may be some bad channels where the AMC selects an MCS with a PER much larger than the target PER. This results in a broken link for a duration that depends on the coherence time of the channel. The solution to this problem is to change the SNR threshold within a safety margin to obtain a more robust MCS selection. However, this results in a reduction in system throughput.
To reduce this safety margin, a PER indicator and an exponential effective SNR mapping (Exp-ESM) method may be used to predict PER performance. In both cases, the scalar is calculated based on knowledge of the CSI. The PER indicator method is based on the following observations: the PER curves achieved for all channels are nearly parallel. An indicator is computed that is mapped to a corresponding distance (dB) from PER performance of an Additive White Gaussian Noise (AWGN) channel. This mapping is achieved by curve fitting each combination of code rate, modulation and information block size. For the (Exp-ESM) method, it consists of deriving a scalar LQM called the exponential effective SNR. This SNR is exactly what is required for the AWGN channel to achieve the same PER as the communication system 100. The way to achieve this effective SNR is to adapt the model to a large number of independent channel realizations based on specific criteria. In general, it is difficult to characterize PER performance with scalar parameters.
A number of parameters may be used to predict PER performance. For example, a method based on using post-detection signal-to-noise ratio (post-detection SNR) may be used to predict PER performance in a MIMO-OFDM system where each stream is encoded separately.
The effective SNR for each stream is then mapped to a corresponding PER that is used to predict the overall PER.
Recently, there has been an increasing emphasis on joint detection and decoding of coded wireless systems by iterative decoding based on Turbo principles. It is an efficient and powerful method of decoding a wireless system since it is close to the limit of an optimal decoder. The method has been applied to wireless systems with intersymbol interference, multiple antennas, multiple carriers, and multiple users. Such a system is basically a cascaded scheme. For any particular channel implementation, the BER (bit error rate) performance of iterative decoding can be characterized by an extrinsic information transfer (EXIT) function.
AMC can also be implemented in MIMO systems using an iterative receiver with ideal CSI, the approximate value of the BER performance of the receiver being based on EXIT analysis. If the error probability of each bit is conditionally independent of the errors of the other bits, PER can be determined by:
PER＝1-(1-BER)B (1)
where B is the number of bits in the packet. For coded systems, however, the BER is usually correlated, i.e., the error probability of one bit depends on the errors of the other bits. Therefore, equation (1) is only a rough approximation of PER.
In the following, a quasi-static block attenuation model for the MIMO-OFDM communication system 100 is assumed. Further assume that each OFDM symbol has NfSub-carriers, each packet having NpOne OFDM symbol. The fading channel is assumed to remain static during each packet transmission. A quasi-static assumption of the channel is not necessary. The embodiments described below can also be used with regularly updated CSI.
According to OFDM, the transmission circuit 104 pairs signal vectorssAn Inverse Fast Fourier Transform (IFFT) is performed. Furthermore, the IFET-transformed signal vector is transmitted via the transmitting antenna 103sPreviously, a cyclic prefix is inserted (cyclic insertion) by the transmission circuit 104. Signal vectorsFor example, from an input data stream that is processed by encoding, interleaving, modulation, and demultiplexed to transmit circuitry 104. Alternatively, the transmit spatial processing may be performed after demultiplexing.
Accordingly, the reception circuit 106 removes the cyclic prefix and performs FFT. In the presence of transmit spatial processing, the receiver 102 pairs the received signal vector at the output of the receive circuit 106rCorresponding inverse processing (i.e., receive spatial processing) is performed. For received signal vectorrOr the results of the received spatial processing (if any), are multiplexed, demodulated, deinterleaved, and decoded to generate a reconstructed data stream corresponding to the input data stream.
Given in the k (k 0, …, N) th equationf-1) sub-carriers and the p (p ═ 0, …, Np-1) frequency domain channel response matrix at OFDM slots:
wherein,
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and
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representing receive and transmit spatial correlation matrices, which are determined by the spacing and angular spread (angle spread) of the MIMO antennas 103, 105. L is the number of resolvable paths of the frequency selective fading channel between the transmit antenna 103 and the receive antenna 105.H lIs independent and in accordance with NC(0，α1 2) A matrix of equi-circularly symmetric complex gaussian distributed elements. Suppose thatH lIndependent for values other than 1. Suppose passing
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Will be provided withH lPower normalization. In this model, it is assumed that there is a uniform linear array at the transmitter 101 and receiver 102. Assume that the relative antenna spacing (measured in number of wavelengths) between the antennas 103, 105 is d for the receiving antenna 105rFor the transmit antenna 103 is dt. Further, the average arrival angle (AoA), the average departure angle (AoD), the reception angle spread, and the transmission angle spread are respectively represented as θr、θt、σr 2And σt 2. The actual AoA and AoD are shown as
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And
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wherein,
and
with the existence of these definitions, it is possible to define,RandSthe (i, j) th element of (a) is given by:
Ri，j＝exp[-j2π(j-i)drcos(θr)-(2π(j-i)drsin(θr)σr)2/2]，
(3)
Si，j＝exp[-j2π(i-j)dtcos(θt)-(2π(i-j)dtsin(θt)σt)2/2].
when assuming that appropriate cyclic insertion and sampling is present, MIMO-OFDM communication system 100 decouples the frequency selective channel to N according tofOne correlated flat fading channel:
r k[p]＝H k A k s k[p]+n k[p]
(4)
k＝0，…，Nf-1，p＝0，…，Np-1，
wherein
Is receivingThe signal vector of (subscripted by subcarrier number k);
is the complex channel frequency response matrix defined in equation (2);
is a transmitted modulation symbol vector whose elements are selected from a complex scalar constellation S having a unit average power; a. thekSpecifies transmit spatial processing (if any); and
is according to NC(0，N0 I) I.i.d (independent equal distribution) elements of (a).
The output of the receive spatial processing (if any) can be written as:
y k[p]＝B k r k[p]， (5)
wherein, BkIs a transform that represents the received spatial processing.
Hereinafter, to simplify the symbols, the channel matrix isHReceived signal vectorrEtc. omit subscripts k and p.
If the channel state information is fully known at the transmitter 101, the communication channel 108 (associated with a certain subcarrier) can be decomposed into orthogonal spatial channels by singular value decomposition. However, in practice, knowledge of the channel state information at the transmitter 101 is not ideal.
Therefore, the temperature of the molten metal is controlled,
whereinIs a channel matrix of estimates obtained by zero forcing or from MMSE channel estimates,Ξis to estimate the error matrix. Due to the nature of zero-forcing or MMSE channel estimation,
andΞare not relevant.ΞEach element of (1) is a group having NC(0，σe 2) I.i.d, wherein the variance is given by
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It is given. The estimated channel matrix for each subcarrier can be diagonalized by singular value decomposition to:
wherein,and
is an unitary matrix;
is a diagonal matrix whose elements are the estimated channel matrix
The sorted singular values of (a).
Rank N ofmAt most min (N)r，Nt) Its singular value is at most min (N)r，Nt) And is non-zero. These values are expressed as
i＝1，…，Nm. Attention is paid to
Is a complex Wishart matrix
The value of the characteristic of the rank of (c),
given by:
in this embodiment, the communication system 100 is based on singular value decomposition, symbol vectors (transmit signal vectors)sBy multiplying by a matrix
And (3) carrying out transformation:
i.e. the vector fed to the transmitting circuit 104sBy vectorsxAnd (6) replacing. Generated received signal vectorrGiven by:
the receiving space is processedrMultiplication by
Make up, the following forms result:
due to the fact that
Is normalized, thus the noise vectorzAgain, the mean is 0 and the variance is N0 IGaussian (c) in (d).yThe component of (a) is given by:
for the case where the CSI has perfect knowledge, equation (12) indicates that the transmission can be considered to occur over a set of NmOn parallel equivalent (virtual) channels. However, when there is no ideal knowledge of the CSI, since
Not diagonal and therefore the channels are no longer independent. In other words, not only AWGN is present, but also co-channel interference (CCI) from other channels.
Assuming CCI termsIs a gaussian distribution. The ith channel (i ═ 1, …, N)m) Signal to noise ratio (SNR) gamma ofiIs defined as:
in one embodiment, receiver 102 decodes a received signal vector using iterative decoding, the received signal vector being processed by the receive spaceyAnd (4) treating. Iterative decoding of coded MIMO-OFDM systems is based on exchanging outer soft information (extrinsic soft information) between the MIMO receiver (demodulator) and the soft-input-soft-output (SISO) decoder per subcarrier. For each subcarrier, there is mNmVector in equation (11)yCoded bits of an associated transmission, where m is log2(M) represents the number of bits per M-QAM modulation symbol (assuming QAM modulation is used). The receiver (demodulator) generates an extrinsic log-ratio (extrinsic log-ratio) on each coded bit, which is given by:
wherein p isa(ci，l) Is a coded bit c obtained from the output of a SISO decoderi，lA priori probability of. This extrinsic information is then passed to the SISO decoder as a priori input information. The SISO decoder outputs extrinsic information for the coded bits, for example, using BCJR algorithm, and then forwards the extrinsic information to the MIMO receiver (demodulator). This completes the iterative phase of iterative decoding.
The signal vector for reception at each subcarrier is given by the following equation without using singular value decompositionrMaximum A Posteriori (MAP) receiver:
the exponential computational complexity of equation (15) has led to the study of suboptimal, low-complexity SISO receivers based on soft interference cancellation. These receptionsThe device is based on a combination of a linear filter and an interference canceller. For each transmit antenna i, s can be calculated byiSoft estimation of (2):
wherein p isa(si) Is the prior probability fed back by the decoder. After performing the cancellation, the soft output is given by:
wherein,
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is a soft estimate calculated according to equation (16). Next, to further suppress
Can apply the instantaneous linear filter to
A common choice for such a linear filter is the MMSE filter. However, the following analysis and the embodiments of the invention described below also apply to
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Maximal ratio combining (matched filter) receiver.
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e iis N of all zeros except the ith element of 1tA dimension vector. The variance σ is given byi 2：
The output of the ith filter is then passed through siAs an equivalent AWGN channel of its input symbols. The equivalent channel is represented as:
wherein, aiIs the equivalent amplitude of the signal, the noise term biIs a mean of 0 and a variance of
Gaussian random variable of (2). The parameter a is calculated by the following equationiAnd
：
and
SNR gamma of channeliComprises the following steps:
extrinsic information is given by:
in one embodiment of the invention, an adaptive modulation and coding algorithm is performed. The algorithm is based on packet error rate prediction, with or without consideration of channel estimates.
A method of selecting transmission parameters for data transmission according to an embodiment of the present invention is illustrated in fig. 2.
Fig. 2 shows a flow diagram 200 according to an embodiment of the invention.
In 201, a data throughput is determined for each of a plurality of transmission parameter settings, the data throughput being a data throughput expected when the transmission parameter setting is used for data transmission, wherein the data throughput is determined using a pre-generated mapping of transmission parameter settings to data throughputs.
At 202, transmission parameter settings are selected based on the determined desired data throughput.
The transmission parameter settings are selected, for example, to maximize the desired throughput. In one embodiment, the data throughput is determined from an expected error rate when the transmission parameter settings are used for data transmission.
The error rate is determined, for example, using a pre-generated mapping of transmission parameter settings to error rate.
The transmission parameter settings may be selected to comply with a target error rate constraint for data transmission. The target error rate constraint is obtained, for example, from a quality of service constraint for the data transmission.
The target error rate constraint is, for example, a limit on the probability that the actual error rate exceeds a predetermined target error rate.
In one embodiment, the pre-generated mapping of transmission parameter settings to bit error rates is implemented by a look-up table pre-generated from simulation results.
For example, to perform data transmission with the selected transmission parameter settings.
The transmission parameter settings define, for example, at least one of a data transmission mode, a coding rate, a constellation size, a modulation type, and a coding type.
In one embodiment, the desired error rate for the transmission parameter setting is a desired packet error rate for the transmission parameter setting.
The pre-generated mapping of transmission parameter settings to error rates is pre-generated, for example based on an analysis of extrinsic information transfer in a receiver receiving data according to the respective transmission parameter settings.
For example by simulating a pre-generated mapping of transmission parameter settings to bit error rates. A pre-generated mapping of transmission parameters to bit error rates is pre-generated from the simulation results, for example by curve fitting (e.g. by applying a polynomial function to the simulation results).
Different curve fitting functions may be used for different transmission parameter settings. In one embodiment, different curve fitting functions are used for different signal to noise ratio ranges for the same transmission parameter setting.
In one embodiment, a transmission parameter setting is selected by a first communication device, and specification of the selected transmission parameter setting is signaled by the first communication device to a second communication device for data transmission from the second communication device to the first communication device.
The data transmission is, for example, a data transmission in a wireless communication system, such as a multiple-input multiple-output communication (MIMO) system, a multi-carrier (e.g., OFDM) system, a multi-user system (e.g., OFDM or CDMA), or any wireless communication system with inter-symbol interference (ISI).
In one embodiment, the transmission parameter settings specify a number of spatial streams to be selected for data transmission, and the transmission parameter settings are selected based on a predetermined switching table with which previously selected transmission parameter settings are associated, wherein the switching table includes information relating to characteristics of the data transmission when a different number of spatial streams are used than the previously selected transmission parameter settings.
For example, a characteristic of data transmission is the robustness of the data transmission or the expected throughput of the data transmission.
The transmission parameter setting is selected, for example, from a set of candidate transmission parameter settings generated based on a look-up table generated based on at least one of transmission parameter settings used for transmitting received data packets, a signal-to-noise ratio of a communication channel that has received the data packets, a number of transmit and receive antennas used for data transmission, and capabilities of transmitters and receivers associated with the data transmission.
In one embodiment, the predetermined mapping function uses the post-detection signal-to-noise ratio as an input. For example, multiple spatial streams are used for data transmission, and the determination of the post-detection signal-to-noise ratio includes grouping, classifying, or averaging the signal-to-noise ratios of the spatial streams.
In another embodiment, multiple transmit antennas and multiple receive antennas are used for data transmission, and if the number of transmit antennas is different from the number of receive antennas, the signal-to-noise offset is used to determine the post-detection signal-to-noise ratio.
The method described with reference to fig. 2 is implemented, for example, by a data transmission controller as shown in fig. 3.
Fig. 3 shows a data transmission controller 300 according to an embodiment of the invention.
The data transmission controller 300 comprises a determination circuit 301 configured to determine, for each of a plurality of transmission parameter settings, a desired data throughput (e.g., based on a desired bit error rate) when the transmission parameter setting is used for data transmission, wherein the data throughput is determined using a pre-generated mapping of transmission parameter settings to data throughput.
The data transmission controller 300 further comprises a selection circuit configured to select a transmission parameter setting based on the determined desired data throughput.
The circuit may be a hardware circuit (e.g., an integrated circuit) designed for the respective functions, or may also be a programmable unit (e.g., a processor) programmed for the respective functions. The processor may be, for example, a RISC (reduced instruction set computer) processor or a CISC (complex instruction set computer).
The data transmission controller 300 is, for example, part of a communication device 303, e.g., a communication device of a wireless communication system (e.g., a MIMO OFDM communication system).
The data transmission controller 300 (or the communication device 303) may further comprise a transmitter for transmitting data according to the selected transmission parameter setting or for signaling the selected transmission parameter setting to the other communication device as a transmission parameter setting for use by the other communication device for data transmission.
In the following, embodiments are described in which, in order to determine a desired data throughput, a desired error rate is determined, which in this embodiment is a desired Packet Error Rate (PER). In this regard, the received signal vectors are described in more detail with reference to FIG. 4rAnd (4) processing.
Fig. 4 illustrates a demodulation/decoding system 400 according to an embodiment of the present invention.
The demodulation/decoding system 400 is, for example, part of the receiver 102. It will receive the signal vector from the receiving circuit 106rReceive as input 401, or if there is receive spatial processing, the received signal vector that will undergo spatial processingyAs input 401.
The demodulation/decoding system 400 includes a demodulator 402 and a decoder 403. The input 401 is fed to a demodulator 402 and the output of the demodulator 402 is fed to a decoder 403. Note that the output of demodulator 402 may be deinterleaved before feeding it to decoder 403. This is omitted for simplicity.
The demodulator 402 is for example a MIMO demodulator and the decoder 403 is for example a SISO decoder.
The demodulation/decoding system 400 may perform iterative processing on the input 401.
A simple graphical description of the convergence behavior of an iterative decoding algorithm is given by an extrinsic information transfer (EXIT) diagram. Both demodulator 402 and decoder 403 may be characterized by a non-linear EXIT function for mutual information (mutual information) of the coded bits.
For SVD and MMSE based MIMO-OFDM systems, the output of receive circuit 106 (as described above) can be modeled as a plurality of virtual AWGN channels having SNRs given by equations (13) and (23), respectively. Given the extrinsic information distribution of decoder 403, the EXIT function of these virtual channels is described as follows:
de＝f(da，γ) (25)
wherein d iseIs the external mutual information of the demodulator 402, daIs a priori mutual information possible from decoder 403 and y is the SNR of the respective virtual channel.
The EXIT function f is obtained, for example, by a monte-carlo simulation.
Fig. 5 shows an embodiment of the EXIT function for different demodulation.
Fig. 5 shows a function graph 501, 502, 503.
The functional graphs 501, 502, 503 are shown in a coordinate system 500 comprising an x-axis 504 and a y-axis 505. The x-axis coordinate corresponds to SNR and the y-axis coordinate corresponds to de. The first functional graph 501 shows the SNR to d for an AWGN channel (i.e., a channel with additive white Gaussian noise) using QPSK and Gray (Gray) mappingeMapping of (2); the second functional graph 502 shows the SNR to d for AWGN channels using 16QAM and Gray mappingeMapping of (2); and a third functional graph 503 gives the SNR to d for AWGN channels using 64QAM and Gray mappingeTo (3) is performed. daIs assumed to be zero.
Taken together, the functional graphs 501, 502, 503 can be viewed as ranging from modulation type and SNR to deTo (3) is performed.
The EXIT function of decoder 403 is given by:
ce＝g(c a) (26)
wherein,c ais a priori mutual information from the demodulator 402.
Information from demodulatorc aIs a vector because the output of the demodulator corresponds to a plurality of virtual AWGN channels, where the EXIT function of these virtual AWGN channels is given by equation (25).
Thus, the distribution of the prior log-likelihood ratios is a mixture of gaussians:
wherein c ═ 1 denotes the bit value,. mu.i＝J-1(cai) /2, and J (2 μ) are defined as
J(2μ)＝1-∫exp[-(z-μ)2/4/μ]log2[1+exp(-z)]dz (28)
Thus, mutual information is a consistent (coherent) gaussian random variable
Where x is an equiprobable binary random variable x ∈ { ± 1 }.
In addition, canBy decoding an iteration withc aCorrelation is used to estimate the performance of iterative decoding. The encoding is implemented, for example, with a convolutional code. Packet Error Rate (PER) can then be approximated by:
PER＝1-(1-CBER)B/υ (29)
where B is the length of the packet and CBER is the coded Bit Error Rate (BER). In this embodiment, only error events of length v are considered. The CBER function is defined by the formulac aAnd (3) correlation:
CBER＝fCBER(c a) (30)
obtaining function f by simulation and curve fittingCBER(c a). In order to be able to approximately describe the function with reasonable complexity, a reduction is madec aDimension of (d) or SNR. For example, reduction in dimensionality (in other words, resolution or accuracy) is achieved by uniform quantization using four levels. The method is better than the method of takingc aThe mean of (a) is more accurate.
According to one embodiment, a mapping of transmission parameters to packet error rates is pre-generated. For example, the modulation type d shown in FIG. 5 is pre-generatedeBy d (also taking SNR γ as input in this embodiment)eAn expected bit error rate for the modulation type is determined.
To this end, the function f is pre-generated, for example, as described above, for example by simulation and curve fittingCBERThen the function f can be usedCBERThe CBER for a particular transmission parameter setting is determined. The expected packet error rate can be calculated from the CBER using equation (29).
As an example, consider a MIMO-OFDM system in which the communication system 100 is a 16QAM 1/2 rate convolutional code (rate 1/2 volumetric code). An interleaver is randomly selected. The packet length is 4090 or 8186 bits. With NfThe channel has L-6 resolvable multipaths for 64 subcarriers. For the correlation matrices S and R, it is assumed that d is for all pathsr＝dt＝1/2，θr＝θtPi/2 and
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consider a fixed two channel implementation during packet transmission. These implementations were simulated with SVD to determine average PER performance, and these simulations show that the simulated PER performance is close to that predicted by EXIT analysis (GM-4). When channel estimation errors are included, the error margin is about 0.4 dB. The margin of error is mainly caused by the gaussian approximation of CCI in equation (12). Similar results can be observed when using an MMSE receiver.
As another example, consider that each packet transmission encounters a randomly and independently selected channel realization. When an ideal CSI is assumed, the PER performance obtained from the simulation is still close to the performance predicted by the EXIT analysis in the above embodiment. When considering channel estimation errors, an error margin of about 0.35dB may be observed. Furthermore, the above error margin is also applicable to MMSE receivers.
Finally, consider an SVD-based MIMO-OFDM system with non-uniform modulation. The stronger stream is modulated by 64QAM and the weaker stream is modulated by 16 QAM. Perfect channel estimation is assumed. As can be seen from the simulation of measuring PER performance for different packet lengths and antenna numbers, the simulated PER performance is close to the performance predicted by the EXIT analysis described according to the above embodiment.
An example of selecting transmission parameters according to an embodiment of the present invention is described below.
The setting of the transmission parameters is performed, for example, by the receiver 102, and the transmission parameters are related to the usage of the communication channel 108 (e.g., the type of modulation used, the type of code or code rate, the constellation size, or the transmission mode).
The method for setting the transmission parameters in this embodiment is a link adaptation algorithm using an AMC (adaptive modulation and coding) algorithm, the objective of which is to maximize the throughput (data rate) of the communication channel 108 on the condition that the PER is below a certain threshold and the data rate is above a certain limit. A procedure for determining appropriate transmission parameter settings (also denoted as MCS for modulation and coding schemes) is described below with reference to fig. 6.
Fig. 6 shows a flow diagram 600 according to an embodiment of the invention.
The constraint that PER is below a certain threshold and data rate is above a certain threshold may be considered as a QoS (quality of service) constraint.
In 601, a set of MCSs is selected based on QoS constraints and estimated received SNRs and/or noise powers. 601 is implemented, for example, by roughly estimating the number of spatial streams and transmission modes using a look-up table.
At 602, an approximation of PER is determined for each MCS in the set of MCSs, e.g., based on the EXIT analysis described above. The desired throughput is then calculated from the PER approximation.
In 603, the MCS providing the maximum throughput is selected. Each MCS has, for example, an index, and the index of the selected MCS is transmitted to the transmitter 101 through a feedback channel.
The determination of PER for MCS in 602 is based on a pre-generated mapping of MCS (transmission parameter settings) to PER. An embodiment for this PER prediction method is described below with reference to fig. 7.
Fig. 7 shows a flow diagram 700 according to an embodiment of the invention.
In 701, an input to a PER prediction method is provided. These inputs are channel estimates
Channel estimation error sigmae 2Estimated noise power N0And MCS (transmission parameter setting) that the desired PER should be determined.
The output of the receive circuit 106 is modeled as a plurality of virtual AWGN channels having SNR γ given by equation (13) (in the case of SVD) or equation (23) (in the case of MMSE).
In 702, the extrinsic mutual information for the output of each virtual channel is determined according to equation (25).
In 703, external mutual informationc a(including the extrinsic mutual information for all virtual channels) is used to approximate the PER according to equation (29), where CBER is given by equation (30). Function fCBERDepending on the code used. Function fCBERPre-generated by simulation and curve fitting. To approximate the description function f with reasonable complexityCBERCan reducec aDimension (d) of (a). The reduction in dimensionality is achieved, for example, by using a uniform quantizer.
Hereinafter, two examples of link adaptation performed by two communication devices according to embodiments of the present invention are described with reference to fig. 8 and 9.
In the embodiment described with reference to fig. 8, the link adaptation is transmitter based. This means that the transmitter of the data itself selects the MCS (transmission parameter set) for data transmission. In this case, no MCS feedback from the receiver to the transmitter is required.
Fig. 8 shows a flow diagram 800 according to an embodiment of the invention.
Including a first communication device 801, such as an Access Point (AP), and a second communication device 802, such as a mobile Station (STA).
The first communication device 801 and the second communication device 802 have a communication connection over a corresponding (recurring) (e.g. according to a TDD (time division duplex) mode) communication channel. This means that there is a downlink from the first communication channel 801 to the second communication channel 802 and an uplink from the second communication channel 802 to the first communication channel 801. The corresponding properties of the communication channel are applied to the link adaptation method. The downlink is used to select the MCS for the uplink channel and vice versa.
At 803 (t)0Time of day), the first communication device 801 transmits a first data frame 811 according to, for example, the first mcs (mcs a) initially selected.
At 805 (t)1Time of day), the second communication device 802 receives the first data frame 811, and at 805 the second communication device 802 (as a transmitter) selects a second mcs (mcs b) for data transmission to the first communication device 801 using information (e.g., channel state information about the downlink channel) received from the first data frame 811. This means that the second communication device 802 selects the MCS for the uplink channel based on the downlink channel.
At 806 (t)2Time of day), the second communication device 802 transmits a second data frame 812 according to the second MCS.
At 807 (t)3Time of day), the first communication device 801 receives the second data frame 812, and in 808, the first communication device 801 (as a transmitter) selects a third mcs (mcs c) for downlink transmission using the information received from the second data frame 812.
At 809, the first communication device 801 transmits a third data frame 813 according to the third MCS, the third data frame 813 being transmitted by the second communication device 802 (t)5Time of day), etc.
In the embodiment described with reference to fig. 9, the link adaptation is transmitter based. This means that the transmitter of the data uses the MCS signalled by the receiver for the data transmission. For this, the communication channels need not be corresponding, since the MCS selection is done in the receiver.
Fig. 9 shows a flow diagram 900 according to an embodiment of the invention.
Similar to fig. 8, a first communication device 901 (e.g., an Access Point (AP)) and a second communication device 902 (e.g., a mobile Station (STA)) are included.
The first communication device 901 and the second communication device 902 have communication connections through a communication channel, which does not need to correspond unlike the case of fig. 8.
At 903 (t)0Time), the first communication device 901 transmits the first data frame 911 to the second communication device 901 according to the first mcs (mcs x). The first data frame 911 includes a specification of a second MCS (MCS X1), which is a Feedback (FB) rate of MCS X1 recommended by the second communication device 902. This provision can be considered as an initial feedback.
At 904 (t)1Time), the second communication device 902 receives the first data frame 911, and at 905 the second communication device 902 selects the third MCS (mcsy) to be used by the first communication device 901 as the MCS for the next data transmission from the first communication device 901 to the second communication device 902. This means that the receiver (in this case the second communication device 902) indicates to the transmitter (in this case the first communication device 901) according to which MCS the data should (or is suggested to) be transmitted.
At 906 (t)2Time of day), the second communication device 905 transmits the second data frame 912 according to the second MCS X1 recommended by the first communication device 901 in the first data frame 911. The second data frame 912 includes a specification of a third MCS (e.g., in a Feedback (FB) field of the second data frame 912).
At 907 (t)3Time), the first communication device receives the second data frame 912 and at 908, the first communication device selects a fourth MCS (MCS z) that the first communication device recommends for the next data transmission from the second communication device 902 to the first communication device 901. At 909 (t)4Time of day), a third data frame 913 including a specification for a fourth MCS is transmitted from the first communication device 901 to the second communication device 902 according to the third MCS, and the third data frame 913 is received by the second communication device 902 (t) and5time of day), etc.
According to one embodiment of the invention, the MCS selection algorithm is used to solve the following optimization problem:
max rMCS(1-PERMCS)
s.t. Pr(PERMCS＞PERt arget)≤0.05 (31)
rMCS∈MCSset，
wherein for a given (e.g., preselected) set of MCSs forming an MCSsetAll MCS of (1) to achieve maximization, rMCSIs the data rate, PERMCSIs the packet error rate of each MCS.
For example, the probability of outage (outage probability) Pr(PERMCS＞PERt arg et) Is set to less than 5%.
In the following, embodiments of MCS selection algorithms performed by the communication device are shown in fig. 8 and 9, including PER prediction as explained with reference to fig. 10.
Fig. 10 shows a data flow diagram 1000 of MCS selection according to an embodiment of the invention.
In this embodiment, the MCS selection algorithm consists of three parts: the structure of MCS set, MCS search algorithm and MCS switching algorithm.
In 1001, a commonly available MCS set for two communication devices 801, 901, 802, 902 is constructed (e.g., in the form of an MCS table). In this regard, the system parameters extracted in 1006 may be considered. This set is very large because it may contain MCSs with different numbers of spatial streams. To reduce the scope of the MCS search, in one embodiment, the search starts with a (sub) set of MCSs for which the same number of spatial streams will be used, based on the MCS used for the previous data transmission (e.g., the MCS used to transmit the last received data packet). For these MCS with twice the rate, the MCS set implemented may be simplified by selecting the MCS with the best PER performance among all MCS with the same rate.
If there are MCSs with the same data rate (theoretically, i.e., without considering packet errors), the MCS set to be implemented can be simplified by selecting an MCS with the best PER performance among all MCSs with the same data rate.
In order to search for the optimal MCS in the MCS set, several different methods may be performed. For example, if the MCS set is small, an exhaustive search may be performed. At 1002, each MCS is tested with a PER prediction algorithm (e.g., the PER prediction algorithm described with reference to fig. 7) in accordance with the search direction in the MCS set. The search is stopped, for example, when a suitable choice for the MCS is found, and the MCS is selected for data transmission in 1007.
A simplified approach is to arrange the MCSs in a table such that their data rates are arranged in descending order. The search starts, for example, from the middle entry of the table, or from the MCS selected for the last data transmission.
The MCS search may be limited to MCSs with the same number of spatial streams for complexity reduction. To further increase throughput performance, a switch may be made from a selected MCS to another MCS that should use a different number of streams. In this case, first, throughput performance curves are generated for all MCSs with the same number of spatial streams. Based on these curves, the switching is achieved by means of a look-up table. The selected MCS can be replaced with an MCS for a smaller number of spatial streams to increase throughput. On the other hand, the MCS for a smaller number of streams may be selected for improved robustness and maintaining the same throughput. By switching, the MCS search range is reduced, and the complexity is reduced, because the PER performance of a plurality of spatial streams does not need to be estimated any more.
The prediction of the PER depends on the channel coherence time and the reliability of the estimate. PER is a function given by:
PER＝f(MCS，N0，H，L)， (32)
wherein N is0Is the variance of the noise, and is,His a channel matrix that gives the current transmission characteristics of the communication channel and L is the packet length.
The PERs are predicted, e.g., for the MCS scheme described above, e.g., used by the communication devices 801, 901, 802, 902 as described below. For MMSE detection, in 1005, for example, by usingFor each spatial stream and subcarrier MIMO module, after synchronization at 1004, SNR γ is determined based on channel estimation performed at 1003i。
Channel estimation errors are included to provide a more accurate PER estimate. An offset may be added in the SNR calculation. The offset is a function of the filter and noise variance of the detector. To reduce the complexity of PER prediction, these values are mapped to the average SNR γ for each spatial streamiThe above. For uniform modulation, where the same modulation is used for all spatial streams, the SNRs for the spatial streams are first classified in the spatial stream for each subcarrier and then averaged over all subcarriers in the frequency domain.
For the beamforming mode, non-uniform modulation may be used. Here, the modulation schemes for different spatial streams may be different. In this case, no classification is required.
For transmitter-based link adaptation, a safety margin is required when the number of transmit and receive antennas is different. This explains the use of the uplink channel to select the MCS for the downlink channel and vice versa. For example, in a system where the access point has 4 antennas and the mobile station has 3 antennas, the uplink will experience receive diversity while the downlink will experience transmit diversity. A safety margin is required for the difference between the diversities.
For example, for transmitter-based link adaptation and asymmetric configuration, an SNR offset may be used.
The output of the detector is modeled as a plurality of virtual AWGN channels whose SNR is defined by gammaiGiven (where i is the number of the virtual channel). Based on the data collected from the simulation, the post-detection SNR γ in 1008 can be achieved by look-up tables for different modulationsiTo detector caiMapping of mutual information. To reduce the storage requirements, the mutual information of the detectors can also be modeled by a cubic polynomial of the form:
wherein p isjIs a predetermined coefficient. Note that γiGiven in dB. Polynomial coefficient pjDetermined from simulations and may take different values for different modulation types. Same modulated p for different SNR valuesjThe values may also be different. Thus, the pair is paired on the spatial stream from γ with a given modulation schemeiGenerated mutual information caiAveraging is performed to give an average value ca.
The behavior of the decoder is also modeled by a cubic polynomial mapping function from extrinsic information ca to the coded bit error rate CBER. CBER is associated with extrinsic information by:
The polynomial function can also be obtained (i.e., pre-generated) offline through monte-carlo simulation. It is also possible to replace the polynomial function with a pre-generated look-up table. For each code rate and type of encoder used, there is a corresponding mapping function. The packet error rate is then estimated from the CBER. For convolutional codes, the PER is approximately:
PER≈1-(1-CBER)L/v (35)
for LDPC-like block codes, the PER is approximated as:
PER≈1-(1-PERB)L/n (36)
wherein, PERBIs the PER of the block code used and n is the length of the codeword. PERBIs related to ca by a cubic polynomial function.
Claims (33)
1. A method of selecting transmission parameters for data transmission, comprising:
determining a data throughput for each of a plurality of transmission parameter settings, the data throughput being a desired data throughput when the transmission parameter setting is used for data transmission, wherein the data throughput is determined by a pre-generated mapping of transmission parameter settings to data throughputs; and
a transmission parameter setting is selected based on the determined data throughput.
2. The method of claim 1, wherein the transmission parameter setting is selected to maximize the desired throughput.
3. The method of claim 1, wherein the data throughput is determined from a bit error rate, the bit error rate being an expected bit error rate when the transmission parameter setting is used for data transmission.
4. A method according to claim 3, wherein the bit error rate is determined by a pre-generated mapping of transmission parameter settings to bit error rates.
5. The method of claim 1, wherein the transmission parameter setting is selected to comply with a target bit error rate constraint for the data transmission.
6. The method of claim 5, wherein the target bit error rate constraint is derived from a quality of service constraint of the data transmission.
7. The method of claim 1, further comprising performing the data transmission with a selected transmission parameter setting.
8. The method of claim 1, wherein the transmission parameter setting defines at least one of a data transmission mode, a coding rate, a constellation size, a modulation type, and a coding type.
9. The method of claim 3, wherein the expected error rate for a transmission parameter setting is an expected packet error rate for the transmission parameter setting.
10. The method of claim 4, wherein the pre-generated mapping of transmission parameter settings to bit error rates is pre-generated based on an analysis of extrinsic information transfer in a receiver that receives data according to the respective transmission parameter settings.
11. The method of claim 4, wherein the pre-generated mapping of transmission parameter settings to bit error rates is pre-generated by simulation.
12. The method of claim 5, wherein the target error rate constraint is a limit on a probability that an actual error rate exceeds a predefined target error rate.
13. The method of claim 11, wherein the pre-generated mapping of transmission parameter settings to bit error rates is implemented by a look-up table pre-generated from the simulation results.
14. The method of claim 11, wherein the pre-generated mapping of transmission parameter settings to bit error rates is pre-generated from the simulation results by curve fitting.
15. The method of claim 14, wherein the curve fitting function is a polynomial.
16. The method of claim 14, wherein different curve fitting functions are used for different transmission parameter settings.
17. The method of claim 14, wherein different curve fitting functions are used for different signal to noise ratio ranges for the same transmission parameter setting.
18. The method of claim 1, wherein the data transmission is a data transmission in a wireless communication system.
19. The method of claim 18, wherein the communication system is a multiple-input multiple-output communication system.
20. The method of claim 16, wherein the communication system is a multiple-input multiple-output system using orthogonal frequency division multiplexing.
21. The method of claim 1, wherein the transmission parameter setting specifies a number of spatial streams to be selected for the data transmission, the transmission parameter setting being selected based on a previously selected transmission parameter setting and a predetermined switching table associated with the previously selected transmission parameter setting, wherein the switching table includes information related to characteristics of the data transmission when a different number of spatial streams are used than the previously selected transmission parameter setting.
22. The method of claim 21, wherein the characteristic of the data transmission is a robustness of the data transmission or the desired throughput of the data transmission.
23. The method of claim 1, wherein the transmission parameter setting is selected from a set of candidate transmission parameters generated based on a look-up table generated based on at least one of: transmission parameter settings for transmitting received data packets, signal-to-noise ratio of a communication channel receiving data packets, number of transmit and receive antennas used for the data transmission, and capabilities of the transmitter and the receiver associated with the data transmission.
24. The method of claim 1, wherein the pre-generated mapping function uses a post-detection signal-to-noise ratio as an input.
25. The method of claim 24, wherein a plurality of spatial streams are used for the data transmission, the determining of the post-detection signal-to-noise ratio comprising at least one of grouping, classifying, or averaging the signal-to-noise ratios of the spatial streams.
26. The method of claim 24, wherein a plurality of transmit antennas and a plurality of receive antennas are used for the data transmission, and wherein the post-detection signal-to-noise ratio is determined using a signal-to-noise ratio offset if the number of transmit antennas is different from the number of receive antennas.
27. A data transmission controller comprising:
a determination circuit configured to determine a data throughput for each of a plurality of transmission parameter settings, the data throughput being a desired throughput when the transmission parameter setting is used for data transmission, wherein the data throughput is determined by a pre-generated mapping of transmission parameter settings to data throughputs; and
a selection circuit configured to select a transmission parameter setting based on the determined data throughput.
28. The data transmission controller of claim 27, being part of a communication device.
29. The data transmission controller of claim 28, the communication device being part of a wireless communication system.
30. The data transmission controller of claim 28, further comprising:
a transmitter for setting transmission data according to the selected transmission parameter or for signaling the selected transmission parameter setting to a communication device as a transmission parameter for data transmission of the communication device.
31. A computer program product which, when executed by a computer, causes the computer to perform a method of selecting transmission parameters for data transmission, the method comprising:
determining a data throughput for each of a plurality of transmission parameter settings, the data throughput being a desired data throughput when the transmission parameter setting is used for data transmission, wherein the data throughput is determined by a pre-generated mapping of transmission parameter settings to data throughputs; and
a transmission parameter setting is selected based on the determined data throughput.
32. A method of estimating a signal-to-noise ratio of a communication channel, comprising:
determining an offset value based on a first value characterizing the noise power and filter coefficients characterizing a filter filtering the received data signal;
determining a second value characterizing the signal-to-noise ratio by the offset value.
33. The method of claim 32, wherein the signal-to-noise ratio is estimated according to:
wherein,
wis a vector of filter coefficients;
σe 2representing the noise power; and
a is determined based on the filter coefficients and the transmission characteristics of the communication channel.
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